{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (285113429)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tanmO1dbWxsDAQNYxmKBYLEa5XI5Dhw7F0NBQ1nE4ipnJH/OSX9W0MFUVZXvfvn2xc+fO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10635bd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='factor(cyl)', y='mpg')) + geom_boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
